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1. Simulating Soybean–Rice Rotation and Irrigation Strategies in Arkansas, USA Using APEX

   https://doi.org/10.3390/su12176822  

   Carroll, Sam R.; Le, Kieu Ngoc; Moreno-García, Beatriz; Runkle, Benjamin R. (September 2020, Sustainability)

   null (Ed.)

   With population growth and resource depletion, maximizing the efficiency of soybean (Glycine max [L.] Merr.) and rice (Oryza sativa L.) cropping systems is urgently needed. The goal of this study was to shed light on precise irrigation amounts and optimal agronomic practices via simulating rice–rice and soybean–rice crop rotations in the Agricultural Policy/Environmental eXtender (APEX) model. The APEX model was calibrated using observations from five fields under soybean–rice rotation in Arkansas from 2017 to 2019 and remote sensing leaf area index (LAI) values to assess modeled vegetation growth. Different irrigation practices were assessed, including conventional flooding (CVF), known as cascade, multiple inlet rice irrigation with polypipe (MIRI), and furrow irrigation (FIR). The amount of water used differed between fields, following each field’s measured or estimated input. Moreover, fields were managed with either continuous flooding (CF) or alternate wetting and drying (AWD) irrigation. Two 20-year scenarios were simulated to test yield changes: (1) between rice–rice and soybean–rice rotation and (2) under reduced irrigation amounts. After calibration with crop yield and LAI, the modeled LAI correlated to the observations with R2 values greater than 0.66, and the percent bias (PBIAS) values were within 32%. The PBIAS and percent difference for modeled versus observed yield were within 2.5% for rice and 15% for soybean. Contrary to expectation, the rice–rice and soybean–rice rotation yields were not statistically significant. The results of the reduced irrigation scenario differed by field, but reducing irrigation beyond 20% from the original amount input by the farmers significantly reduced yields in all fields, except for one field that was over-irrigated. 

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2. A Landscape Evolution Modeling Approach for Predicting Three‐Dimensional Soil Organic Carbon Redistribution in Agricultural Landscapes

   https://doi.org/10.1029/2021JG006616  

   Kwang, Jeffrey S.; Thaler, Evan A.; Quirk, Brendon J.; Quarrier, Caroline L.; Larsen, Isaac J. (February 2022, Journal of Geophysical Research: Biogeosciences)

   Abstract Soil erosion diminishes agricultural productivity by driving the loss of soil organic carbon (SOC). The ability to predict SOC redistribution is important for guiding sustainable agricultural practices and determining the influence of soil erosion on the carbon cycle. Here, we develop a landscape evolution model that couples soil mixing and transport to predict soil loss and SOC patterns within agricultural fields. Our reduced complexity numerical model requires the specification of only two physical parameters: a plow mixing depth,L_p, and a hillslope diffusion coefficient,D. Using topography as an input, the model predicts spatial patterns of surficial SOC concentrations and complex 3D SOC pedostratigraphy. We use soil cores from native prairies to determine initial SOC‐depth relations and the spatial pattern of remote sensing‐derived SOC in adjacent agricultural fields to evaluate the model predictions. The model reproduces spatial patterns of soil loss comparable to those observed in satellite images. Our results indicate that the distribution of soil erosion and SOC in agricultural fields can be predicted using a simple geomorphic model where hillslope diffusion plays a dominant role. Such predictions can aid estimates of carbon burial and evaluate the potential for future soil loss in agricultural landscapes. 

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3. Soybean prediction using computationally efficient Bayesian spatial regression models and satellite imagery

   https://doi.org/10.1002/agj2.21670  

   Fischer, Richard_J; Rekabdarkolaee, Hossein_Moradi; Joshi, Deepak_R; Clay, David_E; Clay, Sharon_A (September 2024, Agronomy Journal)

   Abstract Preharvest yield estimates can be used for harvest planning, marketing, and prescribing in‐season fertilizer and pesticide applications. One approach that is being widely tested is the use of machine learning (ML) or artificial intelligence (AI) algorithms to estimate yields. However, one barrier to the adoption of this approach is that ML/AI algorithms behave as a black block. An alternative approach is to create an algorithm using Bayesian statistics. In Bayesian statistics, prior information is used to help create the algorithm. However, algorithms based on Bayesian statistics are not often computationally efficient. The objective of the current study was to compare the accuracy and computational efficiency of four Bayesian models that used different assumptions to reduce the execution time. In this paper, the Bayesian multiple linear regression (BLR), Bayesian spatial, Bayesian skewed spatial regression, and the Bayesian nearest neighbor Gaussian process (NNGP) models were compared with ML non‐Bayesian random forest model. In this analysis, soybean (Glycine max) yields were the response variable (y), and spaced‐based blue, green, red, and near‐infrared reflectance that was measured with the PlanetScope satellite were the predictor (x). Among the models tested, the Bayesian (NNGP;R²‐testing = 0.485) model, which captures the short‐range correlation, outperformed the (BLR;R²‐testing = 0.02), Bayesian spatial regression (SRM;R²‐testing = 0.087), and Bayesian skewed spatial regression (sSRM;R²‐testing = 0.236) models. However, associated with improved accuracy was an increase in run time from 534 s for the BLR model to 2047 s for the NNGP model. These data show that relatively accurate within‐field yield estimates can be obtained without sacrificing computational efficiency and that the coefficients have biological meaning. However, all Bayesian models had lowerR²values and higher execution times than the random forest model. 

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4. Large Scale Farm Scene Modeling from Remote Sensing Imagery

   https://doi.org/10.1145/3687918  

   Xiao, Zhiqi; Jiang, Hao; Deng, Zhigang; Li, Ran; Han, Wenwei; Wang, Zhaoqi (December 2024, ACM Transactions on Graphics)

   In this paper we propose a scalable framework for large-scale farm scene modeling that utilizes remote sensing data, specifically satellite images. Our approach begins by accurately extracting and categorizing the distributions of various scene elements from satellite images into four distinct layers: fields, trees, roads, and grasslands. For each layer, we introduce a set of controllable Parametric Layout Models (PLMs). These models are capable of learning layout parameters from satellite images, enabling them to generate complex, large-scale farm scenes that closely reproduce reality across multiple scales. Additionally, our framework provides intuitive control for users to adjust layout parameters to simulate different stages of crop growth and planting patterns. This adaptability makes our model an excellent tool for graphics and virtual reality applications. Experimental results demonstrate that our approach can rapidly generate a variety of realistic and highly detailed farm scenes with minimal inputs. 

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5. Forest cover and proximity decrease herbivory and increase crop yield via enhanced natural enemies in soybean fields

   https://doi.org/10.1111/1365-2664.13732  

   González, Ezequiel; Landis, Douglas A.; Knapp, Michal; Valladares, Graciela; Isaac, ed., Marney (August 2020, Journal of Applied Ecology)

   Abstract Non‐crop habitats are essential for sustaining biodiversity of beneficial arthropods in agricultural landscapes, which can increase ecosystem services provision and crop yield. However, their effects on specific crop systems are less clear, such as soybean in South America, where the responses of pests and natural enemies to landscape structure have only recently been studied.Here, we analysed how native forest fragments at local and landscape scales influenced arthropod communities, herbivory and yield in soybean fields in central Argentina. To do this, we selected soybean fields located in agricultural landscapes with varying proportions of forest cover. At two distances (10 and 100 m) from a focal forest fragment, we sampled natural enemy and herbivore arthropods, and measured soybean herbivory and yield. We focused on herbivore diversity, abundance of key soybean pests in the region (caterpillars and stink bugs), and their generalist and specialist natural enemies.Higher abundance of predators, lower herbivory rates and increased yield were found near forests, while overall forest cover in the landscape was positively related with parasitoid and stink bug abundance, soybean yield, and negatively with herbivory. Moreover, yield was positively linked to richness and abundance of generalist and specialist enemies and independent of herbivory according to piecewise Structural Equation Models.Synthesis and applications. Our results show positive effects of native forests on biodiversity and yield in soybean crops, highlighting the need for conservation of forest fragments in agricultural landscapes. Moreover, the relation between natural enemies and crop yield suggests that Chaco forests support a diverse and abundant community of natural enemies that can provide sustained levels of ecosystem services and result in positive effects for farmers. 

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